{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listed Volatility and Variance Derivatives\n",
    "\n",
    "**Dr. Yves J. Hilpisch &mdash; Wiley Finance (2016)**\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading and Settlement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of Variance Futures Terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intraday Trading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.random.standard_normal((10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.,  1.,  1., -1., -1., -1.,  0., -1.,  2.,  2.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
       "      <th>sigma**2</th>\n",
       "      <th>t</th>\n",
       "      <th>DF_t</th>\n",
       "      <th>F_tS</th>\n",
       "      <th>ARMVM_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000038</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000036</td>\n",
       "      <td>2917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>64.580457</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000033</td>\n",
       "      <td>2875</td>\n",
       "      <td>0.040173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000030</td>\n",
       "      <td>2860</td>\n",
       "      <td>0.111062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>2892</td>\n",
       "      <td>0.187647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2  t      DF_t  \\\n",
       "Date                                                                            \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN  1  1.000038   \n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799  2  1.000036   \n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457  3  1.000033   \n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552  4  1.000030   \n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718  5  1.000027   \n",
       "\n",
       "            F_tS   ARMVM_t  \n",
       "Date                        \n",
       "2015-06-01  3000  0.000000  \n",
       "2015-06-02  2917  0.000000  \n",
       "2015-06-03  2875  0.040173  \n",
       "2015-06-04  2860  0.111062  \n",
       "2015-06-05  2892  0.187647  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "h5 = pd.HDFStore('data/var_data.h5', 'r')\n",
    "data = h5['var_future']\n",
    "h5.close()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.870999999999999"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = data['t'][-1]\n",
    "T\n",
    "sigma_K = data['V6I1'][0]\n",
    "sigma_K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['F_ti'] = 3000.0\n",
    "for t in data.index[2:]:\n",
    "    data.loc[t, 'F_ti'] = data['DF_t'].shift(1)[t] * (\n",
    "                    (data['t'][t]  * data['sigma**2'].shift(1)[t]\n",
    "                  + (T - data['t'][t]) * data['V6I1'].shift(1)[t] ** 2) / T\n",
    "                  - sigma_K ** 2) - data['ARMVM_t'].shift(1)[t] + 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
       "      <th>sigma**2</th>\n",
       "      <th>t</th>\n",
       "      <th>DF_t</th>\n",
       "      <th>F_tS</th>\n",
       "      <th>ARMVM_t</th>\n",
       "      <th>F_ti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000038</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000036</td>\n",
       "      <td>2917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>64.580457</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000033</td>\n",
       "      <td>2875</td>\n",
       "      <td>0.040173</td>\n",
       "      <td>2875.140910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000030</td>\n",
       "      <td>2860</td>\n",
       "      <td>0.111062</td>\n",
       "      <td>2835.844331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>2892</td>\n",
       "      <td>0.187647</td>\n",
       "      <td>2820.555454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>214.408816</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000025</td>\n",
       "      <td>2863</td>\n",
       "      <td>0.236836</td>\n",
       "      <td>2853.797523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>183.323049</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000022</td>\n",
       "      <td>2799</td>\n",
       "      <td>0.305743</td>\n",
       "      <td>2827.984087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.019960</td>\n",
       "      <td>300.555651</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000019</td>\n",
       "      <td>2802</td>\n",
       "      <td>0.406065</td>\n",
       "      <td>2763.381425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.007185</td>\n",
       "      <td>279.249096</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000016</td>\n",
       "      <td>2776</td>\n",
       "      <td>0.497784</td>\n",
       "      <td>2778.130966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.013931</td>\n",
       "      <td>302.564935</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000014</td>\n",
       "      <td>2827</td>\n",
       "      <td>0.604188</td>\n",
       "      <td>2748.494268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>-0.018644</td>\n",
       "      <td>359.901600</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000011</td>\n",
       "      <td>2912</td>\n",
       "      <td>0.689681</td>\n",
       "      <td>2788.339034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>332.134168</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000008</td>\n",
       "      <td>2858</td>\n",
       "      <td>0.730901</td>\n",
       "      <td>2856.790440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.007360</td>\n",
       "      <td>315.833038</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000005</td>\n",
       "      <td>2764</td>\n",
       "      <td>0.800957</td>\n",
       "      <td>2792.748076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>299.246550</td>\n",
       "      <td>14</td>\n",
       "      <td>1.000003</td>\n",
       "      <td>2689</td>\n",
       "      <td>0.903601</td>\n",
       "      <td>2705.047722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>278.303868</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2607</td>\n",
       "      <td>1.048771</td>\n",
       "      <td>2629.033294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2   t  \\\n",
       "Date                                                                   \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN   1   \n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799   2   \n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457   3   \n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552   4   \n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718   5   \n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127 -0.011951  214.408816   6   \n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126 -0.003327  183.323049   7   \n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117  0.019960  300.555651   8   \n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120  0.007185  279.249096   9   \n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125 -0.013931  302.564935  10   \n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119 -0.018644  359.901600  11   \n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125  0.004649  332.134168  12   \n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110 -0.007360  315.833038  13   \n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118  0.006306  299.246550  14   \n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120  0.001549  278.303868  15   \n",
       "\n",
       "                DF_t  F_tS   ARMVM_t         F_ti  \n",
       "Date                                               \n",
       "2015-06-01  1.000038  3000  0.000000  3000.000000  \n",
       "2015-06-02  1.000036  2917  0.000000  3000.000000  \n",
       "2015-06-03  1.000033  2875  0.040173  2875.140910  \n",
       "2015-06-04  1.000030  2860  0.111062  2835.844331  \n",
       "2015-06-05  1.000027  2892  0.187647  2820.555454  \n",
       "2015-06-08  1.000025  2863  0.236836  2853.797523  \n",
       "2015-06-09  1.000022  2799  0.305743  2827.984087  \n",
       "2015-06-10  1.000019  2802  0.406065  2763.381425  \n",
       "2015-06-11  1.000016  2776  0.497784  2778.130966  \n",
       "2015-06-12  1.000014  2827  0.604188  2748.494268  \n",
       "2015-06-15  1.000011  2912  0.689681  2788.339034  \n",
       "2015-06-16  1.000008  2858  0.730901  2856.790440  \n",
       "2015-06-17  1.000005  2764  0.800957  2792.748076  \n",
       "2015-06-18  1.000003  2689  0.903601  2705.047722  \n",
       "2015-06-19  1.000000  2607  1.048771  2629.033294  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3605.769230769231"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futures = 100000. / (2 * 26) * T / (T - data['t']['2015-06-09'])\n",
    "futures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2827.9840866529353"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_ti = data['F_ti']['2015-06-09']\n",
    "F_ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446.08408975063566"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_t2 = (data['t']['2015-06-09'] * data['sigma**2']['2015-06-09']\n",
    "         + (T - data['t']['2015-06-09']) * 26 ** 2) / T\n",
    "sigma_t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2776.4648130015858"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_t = (data['DF_t']['2015-06-09'] *\n",
    "       (sigma_t2 - data['V6I1']['2015-06-01'] ** 2)\n",
    "              - data['ARMVM_t']['2015-06-09'] + 3000)\n",
    "F_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10011291.393034564"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_ti = futures * F_t\n",
    "P_ti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trade Matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2799"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_tS = data['F_tS']['2015-06-09']\n",
    "F_tS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10092548.076923078"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_t = futures * F_tS\n",
    "P_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81256.683888513595"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_t - P_ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80199.999999999956"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100000 * (data['V6I1']['2015-06-09'] - 26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different Traded Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trad_vols = np.arange(15, 25.01, 1)\n",
    "trad_vols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 205.55075642,  222.08408975,  239.68408975,  258.35075642,\n",
       "        278.08408975,  298.88408975,  320.75075642,  343.68408975,\n",
       "        367.68408975,  392.75075642,  418.88408975])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_t2 = (data['t']['2015-06-09'] * data['sigma**2']['2015-06-09']\n",
    "         + (T - data['t']['2015-06-09']) * trad_vols ** 2) / T\n",
    "sigma_t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2535.92620765,  2552.45990336,  2570.06028912,  2588.72736492,\n",
       "        2608.46113077,  2629.26158666,  2651.12873261,  2674.06256859,\n",
       "        2698.06309463,  2723.13031071,  2749.26421683])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_t = (data['DF_t']['2015-06-09']\n",
    "       * (sigma_t2 - data['V6I1']['2015-06-01'] ** 2)\n",
    "               - data['ARMVM_t']['2015-06-09'] + 3000)\n",
    "F_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 948583.3858882 ,  888966.69461993,  825503.76520532,\n",
       "        758194.59764436,  687039.19193707,  612037.54808344,\n",
       "        533189.66608347,  450495.54593716,  363955.18764451,\n",
       "        273568.59120552,  179335.75662018])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "margins = futures * (F_tS - F_t)\n",
    "margins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns; sns.set()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mA7nAG8Bo53lj+4NOnxCgN7DIWnvAGFNnjIm31lYDo5r0meXsa4kxZgCQb609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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0767d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame({'margins': margins}, index=trad_vols)\n",
    "results.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After the Trade Matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-06-01     NaN\n",
       "2015-06-02   -83.0\n",
       "2015-06-03   -42.0\n",
       "2015-06-04   -15.0\n",
       "2015-06-05    32.0\n",
       "2015-06-08   -29.0\n",
       "2015-06-09   -64.0\n",
       "2015-06-10     3.0\n",
       "2015-06-11   -26.0\n",
       "2015-06-12    51.0\n",
       "2015-06-15    85.0\n",
       "2015-06-16   -54.0\n",
       "2015-06-17   -94.0\n",
       "2015-06-18   -75.0\n",
       "2015-06-19   -82.0\n",
       "Name: F_tS, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_diffs = (data['F_tS'] - data['F_tS'].shift(1))\n",
    "F_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-06-09   -230769.230769\n",
       "2015-06-10     10817.307692\n",
       "2015-06-11    -93750.000000\n",
       "2015-06-12    183894.230769\n",
       "2015-06-15    306490.384615\n",
       "2015-06-16   -194711.538462\n",
       "2015-06-17   -338942.307692\n",
       "2015-06-18   -270432.692308\n",
       "2015-06-19   -295673.076923\n",
       "Name: F_tS, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "margin_t = futures * F_diffs[F_diffs.index >= '2015-06-09']\n",
    "margin_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "      <th>25.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>948583.0</td>\n",
       "      <td>888967.0</td>\n",
       "      <td>825504.0</td>\n",
       "      <td>758195.0</td>\n",
       "      <td>687039.0</td>\n",
       "      <td>612038.0</td>\n",
       "      <td>533190.0</td>\n",
       "      <td>450496.0</td>\n",
       "      <td>363955.0</td>\n",
       "      <td>273569.0</td>\n",
       "      <td>179336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "      <td>10817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "      <td>-93750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "      <td>183894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "      <td>306490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "      <td>-194712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "      <td>-338942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "      <td>-270433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "      <td>-295673.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                15.0      16.0      17.0      18.0      19.0      20.0  \\\n",
       "Date                                                                     \n",
       "2015-06-09  948583.0  888967.0  825504.0  758195.0  687039.0  612038.0   \n",
       "2015-06-10   10817.0   10817.0   10817.0   10817.0   10817.0   10817.0   \n",
       "2015-06-11  -93750.0  -93750.0  -93750.0  -93750.0  -93750.0  -93750.0   \n",
       "2015-06-12  183894.0  183894.0  183894.0  183894.0  183894.0  183894.0   \n",
       "2015-06-15  306490.0  306490.0  306490.0  306490.0  306490.0  306490.0   \n",
       "2015-06-16 -194712.0 -194712.0 -194712.0 -194712.0 -194712.0 -194712.0   \n",
       "2015-06-17 -338942.0 -338942.0 -338942.0 -338942.0 -338942.0 -338942.0   \n",
       "2015-06-18 -270433.0 -270433.0 -270433.0 -270433.0 -270433.0 -270433.0   \n",
       "2015-06-19 -295673.0 -295673.0 -295673.0 -295673.0 -295673.0 -295673.0   \n",
       "\n",
       "                21.0      22.0      23.0      24.0      25.0  \n",
       "Date                                                          \n",
       "2015-06-09  533190.0  450496.0  363955.0  273569.0  179336.0  \n",
       "2015-06-10   10817.0   10817.0   10817.0   10817.0   10817.0  \n",
       "2015-06-11  -93750.0  -93750.0  -93750.0  -93750.0  -93750.0  \n",
       "2015-06-12  183894.0  183894.0  183894.0  183894.0  183894.0  \n",
       "2015-06-15  306490.0  306490.0  306490.0  306490.0  306490.0  \n",
       "2015-06-16 -194712.0 -194712.0 -194712.0 -194712.0 -194712.0  \n",
       "2015-06-17 -338942.0 -338942.0 -338942.0 -338942.0 -338942.0  \n",
       "2015-06-18 -270433.0 -270433.0 -270433.0 -270433.0 -270433.0  \n",
       "2015-06-19 -295673.0 -295673.0 -295673.0 -295673.0 -295673.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(np.tile(margin_t, (len(trad_vols), 1)).T,\n",
    "                       index=margin_t.index,\n",
    "                       columns=trad_vols)\n",
    "results.loc['2015-06-09', :] = margins  # setting the first day margins\n",
    "np.round(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TNHcMcaRzmKmQZgVXlmSyva6ITVLBvEUrAoHg+kMIv8QRwk8guAlYTOpGKBpmf9/HvH/x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EyKZNG9i8eSOzZs0BoFev3k0jfEOGDGXTpg1n1PF4PNTW1jYd19TU4PF4OqbDInLR04ifiIQt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\niIiECQU/ERERkTCh4CciIiISJv4/ezzSdG+JXrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a076810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.cumsum().plot(figsize=(10, 6), title='cumulative P&L');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interest Rate Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market Disruption Events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**DX Analytics** |\n",
    "<a href=\"http://dx-analytics.com\">http://dx-analytics.com</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance Books** |\n",
    "<a href=\"http://books.tpq.io\" target=\"_blank\">http://books.tpq.io</a>\n",
    "\n",
    "**Python for Finance Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">http://training.tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
